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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Direct Air Capture Using Trapped Small Amines in Hierarchical Nanoporous Capsules on Porous Electrospun Fibers (Final Technical Report)

This report summarizes the carbon capture research and development conducted by The State University of New York at Buffalo (UB) and GTI Energy (GTI) for award “DE-FE0031969: Direct Air Capture Using Trapped Small Amines in Hierarchical Nanoporous Capsules on Porous Electrospun Fibers” sponsored by the U.S. Department of Energy (DOE). The objective of this project is to develop an innovative sorbent structure of trapped small amines in HNC embedded in PEF for DAC. This involves tailoring both sorbent and PEF materials to achieve a compact system for DAC with high capacity for CO 2 at concentrations typically available in air and at near ambient conditions. An innovative sorbent structure of trapped small amines in hierarchical nanoporous capsules (HNC) embedded in porous electrospun fibers (PEF) was developed for direct air capture (DAC). This involves tailoring both sorbent and PEF materials to achieve a compact system for DAC with high capacity for CO 2 at concentrations typically available in air and at near ambient conditions. An interfacial polymerization process was developed, which utilized loaded amines inside mesoporous silica and trimesoyl chloride (TMC) dissolved in organic solvents as the precursors, to generate a polyamide (PA) coating layer on mesoporous silica and thus trap amines. Reaction conditions, including TMC concentration, organic solvents, reaction time, etc., for interfacial polymerization were optimized to effectively trap loaded amines, and cyclic heating-cooling operation was conducted to evaluate the coating quality. Larger pore volume mesoporous silica was also synthesized to increase amine loading and thus increase CO 2 capacity. The optimized sorbent material exhibited CO 2 capacity as high as 4.88 mmol/g under humid DAC conditions and negligible loss (<1%) during 10 cyclic heating-cooling operations. The optimized PA-coated sorbent also showed fast adsorption and desorption kinetics, with <20% t1/2 increase compared to uncoated sorbent. PEF fabrication conditions, including organic solvents for dissolving core and shell polymers, voltage, distance from the nozzle to the collection panel, etc. were adjusted to better incorporate HNC. After incorporating the optimized sorbent material into PEF, the structured sorbent had a CO 2 capacity of approximately 4.0 mmol/g under humid DAC conditions, with capacity loss of 0.17% per cycle and t1/2 increase less than 10%. A techno-economic analysis (TEA) for the process design for a DAC system based on our developed sorbent structure of trapped small amines in HNC embedded in PEF was conducted. The process design included process description and major equipment sizing and energy and mass balances in addition to scale-up research results and estimated capture cost. Aspen Adsorption Simulator was used to fit the experimentally measured breakthrough curves and extract equilibrium and kinetic data of the optimized sorbent. Our results indicated that for a DAC plant with CO 2 productivity of 3,000 tonne/year, the levelized cost of CO 2 capture was $\$$612/tonne, with the largest contribution of 44.33% from the fixed operation cost. Increasing CO 2 productivity, while maintaining similar fixed operation cost, is expected to significantly reduce the CO 2 capture cost. A sensitivity study was also conducted to understand the influence of total plant cost, sorbent cost, CO 2 concentration in the feed, sorbent mat lifetime, sorbent regeneration electricity, and adsorption blower pressure drop on the levelized cost of CO 2 capture, revealing a capture cost range of $\$$520-870/tonne.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Microstructure and Composition of Passivating Interfaces in Silicon Heterojunction Solar Modules Weathered in Different Climates

Sanyo/Panasonic patented silicon heterojunction with intrinsic thin layer (HIT) solar cells in the 1990's, which demonstrated world record photovoltaic (PV) efficiency around 2014 and inspired the newer generations of silicon heterojunction technology (SHJ) as well as the current world record back-contact PV cell designs. The high-quality passivation strategy, utilizing ultra-thin layers of hydrogenated amorphous silicon (a-Si:H), leads to high voltages and long carrier lifetimes, but these qualities may degrade over time as the modules operate outdoors. Here, we investigate local microstructure and composition at the interface layers of cells from HIT modules weathered in a hot, humid climate (Florida, USA) and temperate climate (Colorado, USA) for 10 years. We employ a comprehensive set of high-resolution electron microscopy imaging and spectroscopy to directly resolve structure and composition in these cells down to the nanoscale. We show features such as alignment of the In2O3 transparent conducting oxide and a-Si:H layers on the textured c-Si facets, interfacial oxidation at the a-Si:H/c-Si and In2O3/a-Si:H interfaces, and twinned c-Si resulting from epitaxial growth into the a-Si:H layer. Our results raise potential degradation mechanisms in these outdoor-weathered modules, but the root cause of electrical loss remains uncertain due to the very small changes in aged samples. This study shows that many atomic scale features at the In2O3/a-Si:H/c-Si interfaces are surprisingly robust, and the electrical losses with aging may either be attributed to other pathways or be very sensitive to the subtle chemical and microstructural features observed here.

14 SOLAR ENERGY↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2020

Performance of a CO 2 sorbent for indoor air cleaning applications: Effects of environmental conditions, sorbent aging, and adsorption of co-occurring formaldehyde

Indoor air cleaning systems that incorporate CO 2 sorbent materials enable HVAC load shifting and efficiency improvements. This study developed a bench-scale experimental system to evaluate the performance of a sorbent under controlled operation conditions. A thermostatic holder containing 3.15 g sorbent was connected to a manifold that delivered CO 2 -enriched air at a known temperature and relative humidity (RH). The air stream was also enriched with 0.8-2.1 ppm formaldehyde. The CO 2 concentration was monitored in real-time upstream and downstream of the sorbent, and integrated formaldehyde samples were collected at different times using DNPH-coated silica cartridges. Sorbent regeneration was carried out by circulating clean air in countercurrent. Almost 200 loading/regeneration cycles were performed in the span of 17 months, from which 104 were carried out at reference test conditions defined by loading with air at 25°C, 38% RH, and 1000 ppm CO 2 , and regenerating with air at 80°C, 3% RH and 400 ppm CO 2 . The working capacity decreased slightly from 43-44 mg CO 2 per g sorbent to 39-40 mg per g over the 17 months. The capacity increased with lower loading temperature (in the range 15-35°C) and higher regeneration temperature, between 40 and 80°C. The CO 2 capacity was not sensitive to the moisture content in the range 6-9 g/m 3 , and decreased slightly when dry air was used. Loading isothermal breakthrough curves were fitted to three simple adsorption models, verifying that pseudo-first-order kinetics appropriately describes the adsorption process. The model predicted that equilibrium capacities decreased with increasing temperature from 15 to 35°C, while adsorption rate constants slightly increased. The formaldehyde adsorption efficiency was 80%-99% in different cycles, corresponding to an average capacity of 86 ± 36 µg/g. Formaldehyde was not quantitatively released during regeneration, but its accumulation on the sorbent did not affect CO 2 adsorption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Carbonation of Alkaline Earth Metal Hydroxides: Structure across Nano- to Mesoscales

The conversion of gaseous CO 2 into a solid constitution through mineralization is an active area of carbon capture, and alkaline earth metal hydroxides (M(OH) 2 , M = Ca 2+ , Mg 2+ ) are frontrunners in this area. As model systems, nanolime samples are excellent templates for the study of this reaction. Here, we have examined these under ambient pressure conditions with controlled humidity and CO 2 . Utilizing a broad range of analytical methods, we first established the purity and structures of the selected materials. We then examined the structural changes due to carbonation, using infrared spectroscopy, X-ray scattering, and neutron scattering. The resulting structural changes are resolved from nanoscale to mesoscale and from early-stage to late-stage carbonation. Ca(OH) 2 and Mg(OH) 2 are found to behave quite differently. As expected from prior work, the carbonation of Ca(OH) 2 is kinetically favored. Further, our structure studies suggest this is due to a facile reaction at the fractal interface of the particles. From early- to late-stage carbonation, there is a consistent increase in the fractal roughness. This is in contrast to Mg(OH) 2 where the same surface evolves into a smooth conformal coating. For this material the major reacting component is at the mesoscale, suggesting globular particle growth or evolving macro-porosity. Because neutron scattering is sensitive to hydrogen content, we expected a significant change as M(OH) 2 evolves to MCO 3 . Such a change is found for Ca(OH) 2 but not for Mg(OH) 2 , providing evidence for the formation of hydrated carbonates for the later material. The formation of a conformal layer along with water-rich carbonate formation is an impediment to the use of Mg(OH) 2 for carbon capture. For energy-efficient carbon capture, it would be desirable to enhance carbonation rates for Mg(OH) 2 , and one possible route would be the use of anhydrous fluids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Unified Framework to Reconcile Different Approaches of Modeling Transpiration Response to Water Stress: Plant Hydraulics, Supply Demand Balance, and Empirical Soil Water Stress Function

Plant responses to water stress is a major uncertainty to predicting terrestrial ecosystem sensitivity to drought. Different approaches have been developed to represent plant water stress. Empirical approaches (the empirical soil water stress (or Beta) function and the supply-demand balance scheme) have been widely used for many decades; more mechanistic based approaches, that is, plant hydraulic models (PHMs), were increasingly adopted in the past decade. However, the relationships between them—and their underlying connections to physical processes—are not sufficiently understood. This limited understanding hinders informed decisions on the necessary complexities needed for different applications, with empirical approaches being mechanistically insufficient, and PHMs often being too complex to constrain. Here we introduce a unified framework for modeling transpiration responses to water stress, within which we demonstrate that empirical approaches are special cases of the full PHM, when the plant hydraulic parameters satisfy certain conditions. We further evaluate their response differences and identify the associated physical processes. Finally, we propose a methodology for assessing the necessity of added complexities of the PHM under various climatic conditions and ecosystem types, with case studies in three typical ecosystems: a humid Midwestern cropland, a semi-arid evergreen needleleaf forest, and an arid grassland. Notably, Beta function overestimates transpiration when VPD is high due to its lack of constraints from hydraulic transport and is therefore insufficient in high VPD environments. With the unified framework, we envision researchers can better understand the mechanistic bases of and the relationships between different approaches and make more informed choices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data from: "Moisture rivals temperature in limiting photosynthesis by trees establishing beyond their cold-edge range limit under ambient and warmed conditions"

This archive contains data files that were used to draw conclusions in “Moisture rivals temperature in limiting photosynthesis by trees establishing beyond their cold-edge range limit under ambient and warmed conditions”, by Moyes et al., 2015. All field research was completed in common garden plots set up as part of the Alpine Treeline Warming Experiment (ATWE) on Niwot Ridge, Colorado, USA.There are two main data file formats in this archive: comma-separated values (.csv), and Microsoft Excel (.xls and .xlsx). .xlsx files can be read using Microsoft Excel and Google Sheets, and .csv files can be read using any simple text editor program, such as TextEdit (Mac) and Notepad (Windows). This .pdf data user’s guide can be read using Adobe Acrobat Reader, or any other compatible software. Seedling photographs and their corresponding leaf area-processed images are available in .jpg/.JPG image format, and can be opened using Preview (Mac) and Photos (Windows). To provide additional spatial context, two types of geospatial files are also published in this data package: ESRI shapefiles (.shp) and .kml files. Shapefiles are compatible with any GIS software able to read the file type (such as QGIS or ESRI’s ArcGIS suite), and .kml files can be opened with Google Earth or Google Maps. Figures 3 and 4 in the publication contain data from Moyes et al. 2013. This publication is cited in the References section in this archive, and data files can be accessed via the Alpine Treeline Warming Experiment project portal on ESS-DIVE. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Climate change is altering plant species distributions globally, and warming is expected to promote uphill shifts in mountain trees. However, at many cold-edge range limits, such as alpine treelines in the western United States, tree establishment may be colimited by low temperature and low moisture, making recruitment patterns with warming difficult to predict.- We measured response functions linking carbon (C) assimilation and temperature- and moisture-related microclimatic factors for limber pine (Pinus flexilis) seedlings growing in a heating × watering experiment within and above the alpine treeline. We then extrapolated these response functions using observed microclimate conditions to estimate the net effects of warming and associated soil drying on C assimilation across an entire growing season.- Moisture and temperature limitations were each estimated to reduce potential growing season C gain from a theoretical upper limit by 15–30% (c. 50% combined). Warming above current treeline conditions provided relatively little benefit to modeled net assimilation, whereas assimilation was sensitive to either wetter or drier conditions.- Summer precipitation may be at least as important as temperature in constraining C gain by establishing subalpine trees at and above current alpine treelines as seasonally dry subalpine and alpine ecosystems continue to warm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Submicrometer spectromicroscopy of UO 2 aged under high humidity conditions

We report the oxidation of uranium dioxide is a complicated process, depending on factors including humidity, temperature, and microstructure. To further determine the characteristics of this process, UO 2 particles were allowed to age and agglomerate under 98% relative humidity at room temperature for 378 days. A focused ion beam (FIB) section of this agglomeration was then measured at the O K-edge, U N 5 -edge, and C K-edge using the scanning transmission x-ray microscope (STXM) at the Advanced Light Source. O K-edge and U N 5 -edge x-ray absorption measurements allowed for the elemental and chemical species mapping of the agglomerates and indicated the formation of schoepite at the submicrometer scale in specific locations. Non-negative matrix factorization was employed to elucidate the main components at the O K-edge, which were uranyl (schoepite) formed primarily at the interface of the sample with controlled atmosphere, a UO 2 -like bulk component present in the majority of the sample, and an oxygen species present at the surface of the FIB section, which is likely adsorbed water. STXM spectromicroscopy measurements at the U N 5 -edge measurements also confirmed the location of oxidized uranium. This analysis is a valuable insight into the formation of schoepite on UO 2 and shows the sensitivity to and utility of STXM spectromicroscopy for uranium speciation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Direct Measurement of Small Particle Growth and Aging at the Atmospheric Radiation Measurement Southern Great Plains Observatory Field Campaign Report

Two identical Captive Aerosol Growth and Evolution (CAGE) chambers were operated at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory in the late summer and fall of 2021. The field study was scheduled to begin in spring, 2020, but was delayed because of the pandemic. CAGE chambers are designed to expose particles to an environment that mirrors that of the surroundings with or without a controlled perturbation designed to assess a sensitivity. The analysis here focuses on roughly the last two months of the overall study period during which measurements were almost continuous and when several perturbation experiments were conducted. Though the utility of a dual-chamber system is the ability to measure the influence of a single change on top of ambient conditions, both chambers were initially operated in the same way, with ambient air pulled through the gas exchange channel in both and ammonium sulfate particles injected into both. The similar time-dependent particle growth observed in the chambers for those periods provides confidence in differences observed during the subsequent perturbation experiments. The growth rate of particles in the reference chamber into which only ammonium sulfate particles were injected and for which only ambient air was pulled through were used to describe time-of-day averages. The average growth rate was highest in the evening and in the morning after sunrise and lowest in the late afternoon. The sensitivity of particle growth to secondary aerosol precursor gases was studied by adding them at a controlled rate to the ambient air flow pulled through one of the two chambers. Addition of 5 ppb of α-pinene resulted in an average particle growth rate of 4.4 nm hr -1 , compared with that of just 0.8 nm hr -1 in the reference chamber. The added α-pinene also triggered one new particle formation (NPF) event in the early evening just before sunset and another in the morning just after sunrise. Similarly, addition of 5 ppb of SO 2 to one chamber led to a pair of NPF events and to increased particle growth rate, though unlike the impact of added α-pinene, growth was enhanced only during the daytime when OH concentration is highest. The influence of aerosol liquid water on secondary aerosol formation and particle growth was investigated by injecting ammonium sulfate seed particles into one chamber and potassium sulfate particles into the other. Particles were injected into the two chambers four times over a 1.5-day period. The chamber relative humidity (RH) history during and following each injection was used to determine whether each particle type was crystalline or aqueous. For the case when both particle types remained crystalline throughout the period, they were tracked and for the case when they remained aqueous, the magnitude and time-dependence of the growth of both were almost exactly the same. For the other two cases the ammonium sulfate particles deliquesced upon injection and remained aqueous, while the potassium sulfate particles remained crystalline. For those two cases, the aqueous particles grew substantially faster than did the crystalline particles, providing evidence of the role of aerosol liquid water on secondary aerosol formation and particle growth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The effect of repeated hurricanes on the age of organic carbon in humid tropical forest soil

Abstract Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30‐year period that spanned repeated hurricanes. We used a nonlinear matrix model of soil C pools and fluxes (“soilR”) and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times of soil C in 0–10 cm and 35–60 cm depths relative to a modeled control soil with constant inputs over the 30‐year period. Between 1988 and 2018, the occluded C stock increased and δ 13 C in all pools decreased, while changes in particulate and mineral‐associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris‐deposited C, and possible microbial scavenging of old and young C in the particulate and mineral‐associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that the increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, making soil C more sensitive to future tropical forest stressors.

14C↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Sensitivity of tropical orographic precipitation to wind speed with implications for future projections

Abstract. Some of the rainiest regions on Earth lie upstream of tropical mountains, where the interaction of prevailing winds with orography produces frequent precipitating convection. Yet the response of tropical orographic precipitation to the large-scale wind and temperature variations induced by anthropogenic climate change remains largely unconstrained. Here, we quantify the sensitivity of tropical orographic precipitation to background cross-slope wind using theory, idealized simulations, and observations. We build on a recently developed theoretical framework that characterizes the orographic enhancement of seasonal mean precipitation, relative to upstream regions, as a response of convection to cooling and moistening of the lower free troposphere by stationary orographic gravity waves. Using this framework and convection-permitting simulations, we show that higher cross-slope wind speeds deepen the penetration of the cool and moist gravity wave perturbation upstream of orography, resulting in a mean rainfall increase of 20 % (m s−1)−1 to 30 % (m s−1)−1 increase in cross-slope wind speed. Additionally, we show that orographic precipitation in five tropical regions exhibits a similar dependence on changes in cross-slope wind at both seasonal and daily timescales. Given next-century changes in large-scale winds around tropical orography projected by global climate models, this strong scaling rate implies wind-induced changes in some of Earth's rainiest regions that are comparable with any produced directly by increases in global mean temperature and humidity.

Nicolas, Quentin (ORCID:000000024116973X)↗
DOE OSTI2024

Effects of Pollen on Hydrometeors and Precipitation in a Convective System

Anemophilous (wind-driven) pollen is one type of primary biological aerosol particle, which can rupture under high humidity conditions and form smaller sub-pollen particles (SPPs). Both pollen and SPPs can reach the upper troposphere under convective conditions, acting as cloud condensation nuclei (CCN) and ice nucleating particles (INPs), thus influencing cloud formation and precipitation. However, the impacts of these biological aerosols on cold cloud formation and local climate remain unclear as there are large uncertainties on their emission flux and ice nucleating abilities. Here, we incorporate pollen emission and rupture processes in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) simulations and update the Morrison microphysics scheme within WRF-Chem using aerosol-aware INP parameterizations to account for pollen in addition to other anthropogenic and biogenic aerosol. INP parameterizations for pollen and SPP are derived from laboratory experiments. When including pollen rupture rates as observed in a series of chamber studies, SPP concentrations increase, leading to an increase of cloud ice and water by up to 50% and potentially extending the duration of the convective system. Among all simulated hydrometeors, graupel and raindrops exhibit the largest enhancements from the inclusion of SPPs, with intensifying precipitation at the backside of the convective system and a greater spatial extent. Sensitivity simulations indicate that SPPs have a greater effect on cloud microphysical processes than whole pollen grains, and further observational evidence is needed to constrain these processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Environmental Conditions Affecting Global Mesoscale Convective System Occurrence

Abstract The ERA5 environments of mesoscale convective systems (MCSs), tracked from satellite observations, are assessed over a 20-yr period. The use of a large set of MCS tracks allows us to robustly test the sensitivity of the results to factors such as region, latitude, and diurnal cycle. We aim to provide novel information on environments of observed MCSs for assessments of global atmospheric models and to improve their ability to simulate MCSs. Statistical analysis of all tracked MCSs is performed in two complementary ways. First, we investigate the environments when an MCS has occurred at different spatial scales before and after MCS formation. Several environmental variables are found to show marked changes before MCS initiation, particularly over land. The vertically integrated moisture flux convergence shows a robust signal across different regions and when considering MCS initiation diurnal cycle. We also found spatial scale dependence of the environments between 200 and 500 km, providing new evidence of a natural length scale for use with MCS parameterization. In the second analysis, the likelihood of MCS occurrence for given environmental conditions is evaluated, by considering all environments and determining the probability of being in an MCS core or shield region. These are compared to analogous non-MCS environments, allowing discrimination between conditions suitable for MCS and non-MCS occurrence. Three environmental variables are found to be useful predictors of MCS occurrence: total column water vapor, midlevel relative humidity, and total column moisture flux convergence. Such relations could be used as trigger conditions for the parameterization of MCSs, thereby strengthening the dependence of the MCS scheme on the environment. Significance Statement Large storm systems called mesoscale convective systems form across Earth. These are collections of thunderstorms, with associated high-level clouds that produce substantial, lighter rainfall and modulate Earth’s energy balance. They produce hazardous weather conditions, such as floods and high winds, and are responsible for a high percentage of rainfall in many regions globally. We investigate the environmental conditions under which they form, so that we can understand the spatial extent of the environment which is important for their formation, and also where and when the effects of these storms might be felt. The novel information generated here should help improve the representation of these storms in weather and climate models, improving the prediction of rainfall, thunderclouds, and high-level clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Radiative heating rate profiles over the southeast Atlantic Ocean during the 2016 and 2017 biomass burning seasons

Marine boundary layer clouds, including the transition from stratocumulus to cumulus, are poorly represented in numerical weather prediction and general circulation models. Further uncertainties in the cloud structure arise in the presence of biomass burning carbonaceous aerosol, as is the case over the southeast Atlantic Ocean, where biomass burning aerosol is transported from the African continent. As the aerosol plume progresses across the southeast Atlantic Ocean, radiative heating within the aerosol layer has the potential to alter the thermodynamic environment and therefore the cloud structure; however, limited work has been done to quantify this along the trajectory of the aerosol plume in the region. The deployment of the first Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF1) in support of the Layered Atlantic Smoke Interactions with Clouds field campaign provided a unique opportunity to collect observations of cloud and aerosol properties during two consecutive biomass burning seasons during July through October of 2016 and 2017 over Ascension Island (7.96° S, 14.35° W). Using observed profiles of temperature, humidity, and clouds from the field campaign alongside aerosol optical properties from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), as input for the Rapid Radiation Transfer Model (RRTM), profiles of the radiative heating rate due to aerosols and clouds were computed. Radiative heating is also assessed across the southeast Atlantic Ocean using an ensemble of back trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Idealized experiments using the RRTM with and without aerosols and a range of values for the single-scattering albedo (SSA) demonstrate that shortwave (SW) heating within the aerosol layer above Ascension Island can locally range between 2 and 8 K d -1 depending on the aerosol optical properties, though impacts of the aerosol can be felt elsewhere in the atmospheric column. When considered under clear conditions, the aerosol has a cooling effect at the TOA, and based on the observed cloud properties at Ascension Island, the cloud albedo is not large enough to overcome this. Shortwave radiative heating due to biomass burning aerosol is not balanced by additional longwave (LW) cooling, and the net radiative impact results in a stabilization of the lower troposphere. However, these results are extremely sensitive to the single-scattering albedo assumptions in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hollow plasma structure during the breakdown phase of nanosecond pulsed pin–pin discharges generated with overvoltage

Atmospheric pressure nanosecond repetitive pulsed discharges are of interest for applications in combustion, material processing, plasma medicine and gas treatment. Here, the observation of a hollow structure at the ignition stage of such discharge in a pin–pin geometry is reported. Plasma images show that the phenomenon occurs in a variety of gases (helium, humid helium, argon and air) suggesting that it is in first approximation non-species specific. The phenomenon is investigated in detail in pure helium discharges for peak applied voltages between 3 and 6 kV and 200 ns voltage pulse duration by performing time-resolved optical emission spectroscopy measurements of electron density, gas temperature and electric field. All the measurements were performed at the mid-gap during the early stages of the discharge, near the breakdown. The transition from a homogeneous filament to a hollow structure occurs between 4 and 5 kV in helium. Electron density measurements by Stark broadening of the He I line at 492.2 nm show no significant difference in electron densities for the applied voltage between 3 and 6 kV. In all cases, a maximum electron density of about 3.5 × 10 21 m –3 was found. Electric field measurements performed by Stark polarization spectroscopy of the He I transition at 492.2 nm and of its forbidden counterpart show electric field strengths higher than the DC breakdown field of helium at atmospheric pressure consistent with the generation of the discharges with overvoltage. Moreover, a correlation between electric field values and the appearance of the hollow structure is observed and, remarkably, the phenomenon is not sensitive to the pre-ionization level (memory effect). Possible mechanisms for the phenomenon are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗